The drug discovery flowchart can be a long and labour intensive process with dozens of single endpoint assays to characterize compound behaviour. At Recursion Pharmaceuticals, we have developed an image-based drug discovery platform that enables the rapid evaluation of compounds using high-dimensional phenotypic signatures that provide efficacy, undesirable toxicity, and potential cellular MoA, earlier in the flowchart, at the hit finding stage. Diseases are modelled in human cells by addition of specific disease-relevant perturbations such as gene disruption, inflammatory cytokines, infectious agents, and others. The cells are labelled with a proprietary set of cellular stains designed to cover a broad range of morphological features and inform on a large scope of biology. Deep learning and additional computer vision methods are used to extract high-dimensional disease specific signatures from our images which accurately represent distinct and subtle cellular responses to disease perturbants and therapeutic candidates. This unbiased, high-dimensional, phenotypic platform enables us to discover highly disease-specific drug candidates that act through both known and novel biology and allows us to screen disease models in at an unprecedented rate.

Chadwick Davis

Research Scientist IIRecursion Pharmaceuticals

I am part of a team that identifies treatments for a broad spectrum of diseases by generating and analyzing massive amounts of multi-parametric phenotypic screening data. My current role is to seek out new biologies and technologies for implementation in our drug discovery pipeline platform. My prior work was the principal biologist responsible for the design, development, and execution of Recursion's discovery pipeline.